Fishing fleets as ecosystem sentinels
Bibliographic record
Abstract
Marine apex predators are promising sentinels for detecting the ecological impacts of climate variability and change. Fishermen are increasingly recognized as marine apex predators, and there are extensive satellite-based geolocation data on fishing vessel activities. Despite this potential, the utility of fishermen as ecosystem sentinels remains unexamined. Using one million vessel positions from 600 U.S. vessels, we assess the effectiveness of fishermen as sentinels for the ecological impacts of Northeast Pacific marine heatwaves on tuna distribution and availability. Fishermen were skillful predictors of extreme northward shifts for albacore and bluefin tunas, and extreme inshore shifts for albacore. Fishermen signaled low albacore availability over a year in advance of a formal fisheries disaster declaration request. Notably, fishermen also indicated true negatives during marine heatwaves: periods of anomalous warming but stable tuna distribution and availability. This information could aid management of transboundary shifts during marine heatwaves of albacore from U.S. to Canadian waters and bluefin from Mexican to U.S. waters. Advanced warning of fisheries disasters could expedite the delivery of relief funds for struggling communities. The number of Earth-orbiting satellites is exponentially rising, generating a wealth of geospatial information on fishing vessels. This rich and growing resource can signal otherwise unobserved ecological impacts, aiding rapid management responses to climate extremes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".